The disclosed subject matter relates generally to optimizing execution of jobs in a computing environment and, more particularly, to strategically eliminating jobs according to the costs for generating reports associated with the jobs.
Many enterprises rely on business intelligence (BI) reports for day-to-day decision making. BI reports are generated by way of extract, transform, and load (ETL) jobs, which require computing resources (e.g., CPU, memory, etc.) to run. Thus, each ETL job has an operational cost that can be calculated based on the cost of processing resources allocated to that job.
In general, the number of jobs proportionally grows with the number of BI reports generated. If sufficient capacity and proper planning are not present, the computing resources may become overloaded and fail to properly support the jobs. It is desirable to maintain sufficient capacity to support the important jobs, while culling the less important jobs or reports to lower operational costs.
Typically, a human operator examines each report to see if the report meets certain cost-benefit criteria based on which the report and the supporting jobs may be decommissioned. This process is a manual task and expensive. Further, the process typically does not result in substantial savings, due to lack of insight about operational costs of individual reports.